Guide to AI for Modern Software Engineering

Machines now write a great deal of software. Teams are not shipping better systems, and in some measured cases they are shipping more slowly. This textbook/guide explains why, and what to do about it.

Artificial intelligence rarely removes engineering work; it relocates it. When generating code becomes cheap, the constraint moves to reviewing it. When detecting anomalies becomes cheap, the constraint moves to deciding which ones matter. This book follows that pattern across four disciplines usually taught apart DevOps, DevSecOps, MLOps and AIOps and treats them as one subject, because engineers work across all four over a single system. Twenty chapters cover intelligent delivery pipelines, security testing and compliance, the model lifecycle, observability and incident response, and predictive capacity and cost.

Topics and features:

•Grounded throughout in one running example at realistic scale, with worked arithmetic the reader can follow and challenge

•Full instructor materials: teaching plans, case studies, assessments with rubrics, slide decks with speaker notes, and figure alt text for accessibility

•More than two hundred original figures, each explained element by element rather than left to the reader

•A public companion repository of runnable code and end-to-end deliverables

•Six appendices: tool comparison, Python examples, regulatory quick reference, maturity checklists, glossary and further reading

•Honest about evidencewith each chapter stating its limits, and the closing chapter testing its own forecasts

The book is written for postgraduate and final-year undergraduate students on modules in software engineering, DevOps, secure development and machine learning operations. The content also will appeal to software, security, machine learning and site reliability engineers in practice, as well as engineering leaders deciding what to fund (who will find the cost and capacity material directly applicable).

Muthu Ramachandran is Principal Research Consultant at Forti5 Technologies Ltd, United Kingdom, and Visiting Professor Extraordinarius at the University of South Africa. He holds a PhD from Lancaster University, has more than thirty-five years in software engineering research and practice, and has supervised more than thirty doctoral completions.

Provides chapter exercises with solutions, case studies, review checklists, lecturer slides, and an instructor manual Enables readers to build end-to-end deliverables from a companion open repository Provides a rare integrated treatment of AI-augmented engineering disciplines (DevOps, DevSecOps, MLOps and AIOps) More than two hundred original figures, each explained element by element

Autorentext

Prof. Muthu Ramachandran is a software engineer, AI researcher and cybersecurity practitioner with 35+ years of combined academic and industrial experience. He holds a PhD in Software Engineering from Lancaster University (under Prof. Ian Sommerville FREng), an MTech from IIT Madras, and MSc and BSc degrees from Madurai Kamaraj University. His career spans 21 years as Senior and Principal Lecturer at Leeds Beckett University, eight years as Senior Principal Research Scientist at Philips Research Laboratories (UK) on the MultiSpace and ESPRIT EU projects, two years at Liverpool John Moores University, and earlier research at DRDL and ISRO.

He is currently Principal Research Consultant at Forti5 Technologies Ltd in the United Kingdom and Visiting Professor Extraordinarius at the University of South Africa (UNISA), with adjunct professorial appointments at SIIT Thammasat University (Thailand) and Lincoln University College (Malaysia). He is a Fellow of the British Computer Society (FBCS), Fellow of the Institute of Analytics (FIoA), Senior Member of the IEEE and ACM, Senior Fellow of the Higher Education Academy (SFHEA), a certified IASME Cyber Essentials Plus assessor, Editor-in-Chief of the International Journal of Organizational and Collective Intelligence (IGI Global), and Conference Co-Chair of the International Conference on AI and Blockchain in Healthcare (ABH).

His Springer textbooks include the Guide to AI for Cybersecurity (Texts in Computer Science series), of which the present volume is a companion. Scopus author ID 8676632200; widely cited across more than 150 indexed publications. He gives invited and keynote talks regularly at international conferences and corporate events on AI ethics, AI in cybersecurity, and blockchain engineering.

Inhalt

****Part I Foundations.
**.- The AI-Native Software Engineering Revolution.
.- AI and Machine Learning Fundamentals for Engineers.
.- Platform Architecture: Foundations for AI-Augmented Engineering.
**Part II DevOps.
**.- Intelligent CI/CD Pipelines.
.- AI-Driven Deployment and Release Management.
.- Infrastructure Intelligence and Autonomous Scaling.
.- AI-Assisted Software Development and Code Intelligence.
**Part III DevSecOps.
**.- AI-Powered Security Testing and Vulnerability Assessment.
.- Intelligent Threat Modelling and Secure Design.
.- Compliance by Design and Regulatory Automation.
.- AI-Powered Identity, Access, and Supply Chain Security.
**Part IV MLOps.
**.- ML Model Lifecycle Management.
.- Model Deployment, Serving, and Scaling.
.- Model Monitoring, Drift Detection, and Retraining.
.- ML Governance, Ethics, and Responsible AI Operations.
**Part V AIOps.
**.- Intelligent Observability and Anomaly Detection.
.- AI-Driven Incident Management and Root-Cause Analysis.
.- Predictive Operations and Capacity Intelligence.
**Part VI Integration and Future.
**.- The Unified AI-Native Engineering Platform.
.- The Future of AI-Native Software Engineering.

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